Overview

Dataset statistics

Number of variables35
Number of observations164386
Missing cells1373638
Missing cells (%)23.9%
Duplicate rows22193
Duplicate rows (%)13.5%
Total size in memory88.9 MiB
Average record size in memory566.8 B

Variable types

DateTime1
Categorical9
Numeric25

Warnings

Dataset has 22193 (13.5%) duplicate rows Duplicates
mintemp is highly correlated with temp9amHigh correlation
maxtemp is highly correlated with temp3pmHigh correlation
humidity3pm is highly correlated with humidityHigh correlation
pressure9am is highly correlated with pressure3pmHigh correlation
pressure3pm is highly correlated with pressure9amHigh correlation
temp9am is highly correlated with mintempHigh correlation
temp3pm is highly correlated with maxtempHigh correlation
humidity is highly correlated with humidity3pmHigh correlation
evaporation has 74258 (45.2%) missing values Missing
sunshine has 83779 (51.0%) missing values Missing
humidity9am has 2034 (1.2%) missing values Missing
humidity3pm has 5124 (3.1%) missing values Missing
pressure9am has 16301 (9.9%) missing values Missing
pressure3pm has 16273 (9.9%) missing values Missing
cloud9am has 63507 (38.6%) missing values Missing
cloud3pm has 68670 (41.8%) missing values Missing
temp3pm has 4077 (2.5%) missing values Missing
humidity has 5124 (3.1%) missing values Missing
wind_gustdir has 127967 (77.8%) missing values Missing
wind_gustspeed has 127960 (77.8%) missing values Missing
wind_dir9am has 127454 (77.5%) missing values Missing
wind_dir3pm has 125396 (76.3%) missing values Missing
wind_speed9am has 125086 (76.1%) missing values Missing
wind_speed3pm has 125058 (76.1%) missing values Missing
windgustdir has 46997 (28.6%) missing values Missing
windgustspeed has 46944 (28.6%) missing values Missing
winddir9am has 48360 (29.4%) missing values Missing
winddir3pm has 43825 (26.7%) missing values Missing
windspeed9am has 40753 (24.8%) missing values Missing
windspeed3pm has 42911 (26.1%) missing values Missing
rainfall has 104117 (63.3%) zeros Zeros
sunshine has 2553 (1.6%) zeros Zeros
cloud9am has 9450 (5.7%) zeros Zeros
cloud3pm has 5298 (3.2%) zeros Zeros
amntraintmrw has 105038 (63.9%) zeros Zeros
wind_speed9am has 2351 (1.4%) zeros Zeros
windspeed9am has 7570 (4.6%) zeros Zeros

Reproduction

Analysis started2021-05-09 20:42:07.870133
Analysis finished2021-05-09 20:44:57.592629
Duration2 minutes and 49.72 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

date
Date

Distinct3436
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
Minimum2007-11-01 00:00:00
Maximum2017-06-25 00:00:00
2021-05-09T17:44:57.706181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:57.877790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

location
Categorical

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
Canberra
 
3877
Sydney
 
3798
Perth
 
3655
Darwin
 
3653
Hobart
 
3650
Other values (44)
145753 

Length

Max length16
Median length8
Mean length8.695095689
Min length4

Characters and Unicode

Total characters1429352
Distinct characters40
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlbury
2nd rowAlbury
3rd rowAlbury
4th rowAlbury
5th rowAlbury
ValueCountFrequency (%)
Canberra3877
 
2.4%
Sydney3798
 
2.3%
Perth3655
 
2.2%
Darwin3653
 
2.2%
Hobart3650
 
2.2%
Brisbane3618
 
2.2%
Adelaide3549
 
2.2%
Bendigo3491
 
2.1%
Ballarat3489
 
2.1%
Townsville3489
 
2.1%
Other values (39)128117
77.9%
2021-05-09T17:44:58.338697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
canberra3877
 
2.4%
sydney3798
 
2.3%
perth3655
 
2.2%
darwin3653
 
2.2%
hobart3650
 
2.2%
brisbane3618
 
2.2%
adelaide3549
 
2.2%
bendigo3491
 
2.1%
ballarat3489
 
2.1%
townsville3489
 
2.1%
Other values (39)128117
77.9%

Most occurring characters

ValueCountFrequency (%)
a133294
 
9.3%
r132063
 
9.2%
o122635
 
8.6%
e116897
 
8.2%
n101926
 
7.1%
l88420
 
6.2%
i86195
 
6.0%
t67423
 
4.7%
d41751
 
2.9%
s41340
 
2.9%
Other values (30)497408
34.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1207567
84.5%
Uppercase Letter221785
 
15.5%

Most frequent character per category

ValueCountFrequency (%)
a133294
11.0%
r132063
10.9%
o122635
10.2%
e116897
9.7%
n101926
 
8.4%
l88420
 
7.3%
i86195
 
7.1%
t67423
 
5.6%
d41751
 
3.5%
s41340
 
3.4%
Other values (12)275623
22.8%
ValueCountFrequency (%)
A30799
13.9%
W26799
12.1%
C20924
9.4%
M19959
9.0%
S17616
7.9%
P17212
7.8%
N15666
7.1%
B13969
6.3%
G13686
 
6.2%
H10454
 
4.7%
Other values (8)34701
15.6%

Most occurring scripts

ValueCountFrequency (%)
Latin1429352
100.0%

Most frequent character per script

ValueCountFrequency (%)
a133294
 
9.3%
r132063
 
9.2%
o122635
 
8.6%
e116897
 
8.2%
n101926
 
7.1%
l88420
 
6.2%
i86195
 
6.0%
t67423
 
4.7%
d41751
 
2.9%
s41340
 
2.9%
Other values (30)497408
34.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1429352
100.0%

Most frequent character per block

ValueCountFrequency (%)
a133294
 
9.3%
r132063
 
9.2%
o122635
 
8.6%
e116897
 
8.2%
n101926
 
7.1%
l88420
 
6.2%
i86195
 
6.0%
t67423
 
4.7%
d41751
 
2.9%
s41340
 
2.9%
Other values (30)497408
34.8%

mintemp
Real number (ℝ)

HIGH CORRELATION

Distinct389
Distinct (%)0.2%
Missing772
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean12.16611354
Minimum-8.5
Maximum33.9
Zeros177
Zeros (%)0.1%
Memory size2.5 MiB
2021-05-09T17:44:58.510197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-8.5
5-th percentile1.8
Q17.6
median11.9
Q316.8
95-th percentile23.1
Maximum33.9
Range42.4
Interquartile range (IQR)9.2

Descriptive statistics

Standard deviation6.40119073
Coefficient of variation (CV)0.5261491857
Kurtosis-0.4830438876
Mean12.16611354
Median Absolute Deviation (MAD)4.6
Skewness0.04032331322
Sum1990546.5
Variance40.97524276
MonotocityNot monotonic
2021-05-09T17:44:58.655555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.61035
 
0.6%
10.21028
 
0.6%
111024
 
0.6%
10.51013
 
0.6%
101011
 
0.6%
91010
 
0.6%
8.9993
 
0.6%
12992
 
0.6%
10.8990
 
0.6%
10.4987
 
0.6%
Other values (379)153531
93.4%
ValueCountFrequency (%)
-8.51
 
< 0.1%
-8.22
 
< 0.1%
-82
 
< 0.1%
-7.82
 
< 0.1%
-7.62
 
< 0.1%
-7.53
 
< 0.1%
-7.32
 
< 0.1%
-7.21
 
< 0.1%
-7.11
 
< 0.1%
-78
< 0.1%
ValueCountFrequency (%)
33.91
 
< 0.1%
31.91
 
< 0.1%
31.81
 
< 0.1%
31.44
< 0.1%
31.21
 
< 0.1%
311
 
< 0.1%
30.72
< 0.1%
30.51
 
< 0.1%
30.31
 
< 0.1%
30.21
 
< 0.1%

maxtemp
Real number (ℝ)

HIGH CORRELATION

Distinct505
Distinct (%)0.3%
Missing397
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean23.19011092
Minimum-4.8
Maximum48.1
Zeros15
Zeros (%)< 0.1%
Memory size2.5 MiB
2021-05-09T17:44:58.814256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-4.8
5-th percentile12.8
Q117.9
median22.6
Q328.2
95-th percentile35.5
Maximum48.1
Range52.9
Interquartile range (IQR)10.3

Descriptive statistics

Standard deviation7.12029885
Coefficient of variation (CV)0.3070403101
Kurtosis-0.2356615366
Mean23.19011092
Median Absolute Deviation (MAD)5.1
Skewness0.2333731942
Sum3802923.1
Variance50.69865571
MonotocityNot monotonic
2021-05-09T17:44:59.385326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201016
 
0.6%
19960
 
0.6%
19.8950
 
0.6%
20.4943
 
0.6%
20.8933
 
0.6%
21929
 
0.6%
19.5923
 
0.6%
18.9922
 
0.6%
19.4919
 
0.6%
20.2914
 
0.6%
Other values (495)154580
94.0%
ValueCountFrequency (%)
-4.82
< 0.1%
-4.11
 
< 0.1%
-3.81
 
< 0.1%
-3.71
 
< 0.1%
-3.21
 
< 0.1%
-3.12
< 0.1%
-32
< 0.1%
-2.91
 
< 0.1%
-2.71
 
< 0.1%
-2.53
< 0.1%
ValueCountFrequency (%)
48.11
 
< 0.1%
47.33
< 0.1%
472
 
< 0.1%
46.92
 
< 0.1%
46.84
< 0.1%
46.72
 
< 0.1%
46.62
 
< 0.1%
46.51
 
< 0.1%
46.45
< 0.1%
46.33
< 0.1%

rainfall
Real number (ℝ≥0)

ZEROS

Distinct679
Distinct (%)0.4%
Missing1619
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean2.354321822
Minimum0
Maximum371
Zeros104117
Zeros (%)63.3%
Memory size2.5 MiB
2021-05-09T17:44:59.567237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.8
95-th percentile13
Maximum371
Range371
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation8.417734503
Coefficient of variation (CV)3.575439188
Kurtosis172.8685562
Mean2.354321822
Median Absolute Deviation (MAD)0
Skewness9.719707325
Sum383205.9
Variance70.85825416
MonotocityNot monotonic
2021-05-09T17:44:59.732186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0104117
63.3%
0.210172
 
6.2%
0.44314
 
2.6%
0.62917
 
1.8%
0.82313
 
1.4%
12000
 
1.2%
1.21750
 
1.1%
1.41599
 
1.0%
1.61377
 
0.8%
1.81250
 
0.8%
Other values (669)30958
 
18.8%
(Missing)1619
 
1.0%
ValueCountFrequency (%)
0104117
63.3%
0.1197
 
0.1%
0.210172
 
6.2%
0.384
 
0.1%
0.44314
 
2.6%
0.551
 
< 0.1%
0.62917
 
1.8%
0.716
 
< 0.1%
0.82313
 
1.4%
0.919
 
< 0.1%
ValueCountFrequency (%)
3711
< 0.1%
367.61
< 0.1%
278.41
< 0.1%
268.61
< 0.1%
247.21
< 0.1%
2401
< 0.1%
236.81
< 0.1%
2251
< 0.1%
219.62
< 0.1%
216.31
< 0.1%

evaporation
Real number (ℝ≥0)

MISSING

Distinct356
Distinct (%)0.4%
Missing74258
Missing (%)45.2%
Infinite0
Infinite (%)0.0%
Mean5.494618764
Minimum0
Maximum145
Zeros266
Zeros (%)0.2%
Memory size2.5 MiB
2021-05-09T17:44:59.925991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.6
median4.8
Q37.4
95-th percentile12
Maximum145
Range145
Interquartile range (IQR)4.8

Descriptive statistics

Standard deviation4.275539083
Coefficient of variation (CV)0.7781320718
Kurtosis55.45145963
Mean5.494618764
Median Absolute Deviation (MAD)2.4
Skewness4.122042565
Sum495219
Variance18.28023445
MonotocityNot monotonic
2021-05-09T17:45:00.087344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43744
 
2.3%
82956
 
1.8%
2.22275
 
1.4%
22233
 
1.4%
2.42177
 
1.3%
2.62170
 
1.3%
32160
 
1.3%
3.42150
 
1.3%
1.82146
 
1.3%
3.22110
 
1.3%
Other values (346)66007
40.2%
(Missing)74258
45.2%
ValueCountFrequency (%)
0266
 
0.2%
0.113
 
< 0.1%
0.2527
 
0.3%
0.316
 
< 0.1%
0.4820
0.5%
0.516
 
< 0.1%
0.61188
0.7%
0.727
 
< 0.1%
0.81480
0.9%
0.933
 
< 0.1%
ValueCountFrequency (%)
1452
< 0.1%
86.22
< 0.1%
82.41
< 0.1%
81.21
< 0.1%
77.31
< 0.1%
74.82
< 0.1%
72.21
< 0.1%
70.41
< 0.1%
701
< 0.1%
68.82
< 0.1%

sunshine
Real number (ℝ≥0)

MISSING
ZEROS

Distinct145
Distinct (%)0.2%
Missing83779
Missing (%)51.0%
Infinite0
Infinite (%)0.0%
Mean7.607071346
Minimum0
Maximum14.5
Zeros2553
Zeros (%)1.6%
Memory size2.5 MiB
2021-05-09T17:45:00.275217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q14.9
median8.4
Q310.6
95-th percentile12.8
Maximum14.5
Range14.5
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation3.778323829
Coefficient of variation (CV)0.4966857358
Kurtosis-0.8196559822
Mean7.607071346
Median Absolute Deviation (MAD)2.6
Skewness-0.5002194816
Sum613183.2
Variance14.27573095
MonotocityNot monotonic
2021-05-09T17:45:00.436238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02553
 
1.6%
10.71160
 
0.7%
111153
 
0.7%
10.81138
 
0.7%
10.51109
 
0.7%
10.91099
 
0.7%
10.31077
 
0.7%
10.21059
 
0.6%
101057
 
0.6%
11.11043
 
0.6%
Other values (135)68159
41.5%
(Missing)83779
51.0%
ValueCountFrequency (%)
02553
1.6%
0.1573
 
0.3%
0.2570
 
0.3%
0.3453
 
0.3%
0.4347
 
0.2%
0.5351
 
0.2%
0.6304
 
0.2%
0.7369
 
0.2%
0.8342
 
0.2%
0.9343
 
0.2%
ValueCountFrequency (%)
14.51
 
< 0.1%
14.34
 
< 0.1%
14.22
 
< 0.1%
14.17
 
< 0.1%
1416
 
< 0.1%
13.923
 
< 0.1%
13.857
 
< 0.1%
13.7120
0.1%
13.6184
0.1%
13.5193
0.1%

humidity9am
Real number (ℝ≥0)

MISSING

Distinct101
Distinct (%)0.1%
Missing2034
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean69.08449542
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Memory size2.5 MiB
2021-05-09T17:45:00.634102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q157
median70
Q383
95-th percentile98
Maximum100
Range100
Interquartile range (IQR)26

Descriptive statistics

Standard deviation18.99304951
Coefficient of variation (CV)0.2749249219
Kurtosis-0.05275203679
Mean69.08449542
Median Absolute Deviation (MAD)13
Skewness-0.4796766061
Sum11216006
Variance360.7359295
MonotocityNot monotonic
2021-05-09T17:45:00.804393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
993815
 
2.3%
703481
 
2.1%
1003464
 
2.1%
653416
 
2.1%
683413
 
2.1%
693408
 
2.1%
713378
 
2.1%
663371
 
2.1%
723332
 
2.0%
673326
 
2.0%
Other values (91)127948
77.8%
ValueCountFrequency (%)
01
 
< 0.1%
15
 
< 0.1%
28
 
< 0.1%
310
 
< 0.1%
422
 
< 0.1%
527
 
< 0.1%
640
< 0.1%
746
< 0.1%
856
< 0.1%
977
< 0.1%
ValueCountFrequency (%)
1003464
2.1%
993815
2.3%
982511
1.5%
972118
1.3%
961862
1.1%
951854
1.1%
942042
1.2%
932135
1.3%
922023
1.2%
912152
1.3%

humidity3pm
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct101
Distinct (%)0.1%
Missing5124
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean51.66956336
Minimum0
Maximum100
Zeros4
Zeros (%)< 0.1%
Memory size2.5 MiB
2021-05-09T17:45:00.983225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q137
median52
Q366
95-th percentile88
Maximum100
Range100
Interquartile range (IQR)29

Descriptive statistics

Standard deviation20.7609592
Coefficient of variation (CV)0.4018024897
Kurtosis-0.5074502112
Mean51.66956336
Median Absolute Deviation (MAD)14
Skewness0.03766029493
Sum8228998
Variance431.017427
MonotocityNot monotonic
2021-05-09T17:45:01.157637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
573130
 
1.9%
523122
 
1.9%
553094
 
1.9%
593031
 
1.8%
533031
 
1.8%
563009
 
1.8%
583006
 
1.8%
502973
 
1.8%
602969
 
1.8%
542963
 
1.8%
Other values (91)128934
78.4%
(Missing)5124
 
3.1%
ValueCountFrequency (%)
04
 
< 0.1%
126
 
< 0.1%
235
 
< 0.1%
365
 
< 0.1%
4118
 
0.1%
5166
 
0.1%
6253
0.2%
7329
0.2%
8451
0.3%
9528
0.3%
ValueCountFrequency (%)
100537
0.3%
99492
0.3%
98688
0.4%
97461
0.3%
96534
0.3%
95519
0.3%
94630
0.4%
93675
0.4%
92742
0.5%
91701
0.4%

pressure9am
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct546
Distinct (%)0.4%
Missing16301
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean1017.645456
Minimum980.5
Maximum1041
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-05-09T17:45:01.337419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum980.5
5-th percentile1006.1
Q11012.9
median1017.6
Q31022.4
95-th percentile1029.5
Maximum1041
Range60.5
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation7.129290981
Coefficient of variation (CV)0.007005672694
Kurtosis0.2693896181
Mean1017.645456
Median Absolute Deviation (MAD)4.7
Skewness-0.1128570668
Sum150698027.3
Variance50.82678989
MonotocityNot monotonic
2021-05-09T17:45:01.492402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1016.4924
 
0.6%
1017.9908
 
0.6%
1017.3886
 
0.5%
1018883
 
0.5%
1018.7881
 
0.5%
1015.9871
 
0.5%
1015.5866
 
0.5%
1016.3866
 
0.5%
1017.8864
 
0.5%
1015.7862
 
0.5%
Other values (536)139274
84.7%
(Missing)16301
 
9.9%
ValueCountFrequency (%)
980.51
 
< 0.1%
9822
< 0.1%
982.22
< 0.1%
982.32
< 0.1%
982.93
< 0.1%
983.71
 
< 0.1%
983.92
< 0.1%
984.42
< 0.1%
984.63
< 0.1%
9851
 
< 0.1%
ValueCountFrequency (%)
10411
 
< 0.1%
1040.91
 
< 0.1%
1040.62
< 0.1%
1040.51
 
< 0.1%
1040.43
< 0.1%
1040.34
< 0.1%
1040.22
< 0.1%
1040.14
< 0.1%
10401
 
< 0.1%
1039.94
< 0.1%

pressure3pm
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct549
Distinct (%)0.4%
Missing16273
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean1015.245303
Minimum977.1
Maximum1039.6
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-05-09T17:45:01.674372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum977.1
5-th percentile1004
Q11010.4
median1015.2
Q31020
95-th percentile1026.9
Maximum1039.6
Range62.5
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation7.05609818
Coefficient of variation (CV)0.006950141175
Kurtosis0.1551495431
Mean1015.245303
Median Absolute Deviation (MAD)4.8
Skewness-0.05901271552
Sum150371027.5
Variance49.78852152
MonotocityNot monotonic
2021-05-09T17:45:01.837344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1015.5898
 
0.5%
1015.3882
 
0.5%
1015.7874
 
0.5%
1015.1871
 
0.5%
1015.6870
 
0.5%
1015.8867
 
0.5%
1015.4867
 
0.5%
1016860
 
0.5%
1013.5852
 
0.5%
1015.2846
 
0.5%
Other values (539)139426
84.8%
(Missing)16273
 
9.9%
ValueCountFrequency (%)
977.12
< 0.1%
978.22
< 0.1%
9791
< 0.1%
980.22
< 0.1%
981.21
< 0.1%
981.41
< 0.1%
981.92
< 0.1%
982.21
< 0.1%
982.62
< 0.1%
982.91
< 0.1%
ValueCountFrequency (%)
1039.61
 
< 0.1%
1038.91
 
< 0.1%
1038.51
 
< 0.1%
1038.41
 
< 0.1%
1038.21
 
< 0.1%
10381
 
< 0.1%
1037.92
< 0.1%
1037.82
< 0.1%
1037.73
< 0.1%
1037.61
 
< 0.1%

cloud9am
Real number (ℝ≥0)

MISSING
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing63507
Missing (%)38.6%
Infinite0
Infinite (%)0.0%
Mean4.505328165
Minimum0
Maximum9
Zeros9450
Zeros (%)5.7%
Memory size2.5 MiB
2021-05-09T17:45:02.002378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.893437895
Coefficient of variation (CV)0.6422257799
Kurtosis-1.529387348
Mean4.505328165
Median Absolute Deviation (MAD)3
Skewness-0.2526857912
Sum454493
Variance8.371982852
MonotocityNot monotonic
2021-05-09T17:45:02.112400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
722256
 
13.5%
817743
 
10.8%
117419
 
10.6%
09450
 
5.7%
69086
 
5.5%
27254
 
4.4%
36558
 
4.0%
56178
 
3.8%
44933
 
3.0%
92
 
< 0.1%
(Missing)63507
38.6%
ValueCountFrequency (%)
09450
5.7%
117419
10.6%
27254
 
4.4%
36558
 
4.0%
44933
 
3.0%
56178
 
3.8%
69086
5.5%
722256
13.5%
817743
10.8%
92
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
817743
10.8%
722256
13.5%
69086
5.5%
56178
 
3.8%
44933
 
3.0%
36558
 
4.0%
27254
 
4.4%
117419
10.6%
09450
5.7%

cloud3pm
Real number (ℝ≥0)

MISSING
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing68670
Missing (%)41.8%
Infinite0
Infinite (%)0.0%
Mean4.560992937
Minimum0
Maximum9
Zeros5298
Zeros (%)3.2%
Memory size2.5 MiB
2021-05-09T17:45:02.241305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.731072494
Coefficient of variation (CV)0.5987890206
Kurtosis-1.455244411
Mean4.560992937
Median Absolute Deviation (MAD)2
Skewness-0.2425796017
Sum436560
Variance7.458756968
MonotocityNot monotonic
2021-05-09T17:45:02.365404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
720088
 
12.2%
116538
 
10.1%
815233
 
9.3%
69726
 
5.9%
28005
 
4.9%
37550
 
4.6%
57409
 
4.5%
45868
 
3.6%
05298
 
3.2%
91
 
< 0.1%
(Missing)68670
41.8%
ValueCountFrequency (%)
05298
 
3.2%
116538
10.1%
28005
 
4.9%
37550
 
4.6%
45868
 
3.6%
57409
 
4.5%
69726
5.9%
720088
12.2%
815233
9.3%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
815233
9.3%
720088
12.2%
69726
5.9%
57409
 
4.5%
45868
 
3.6%
37550
 
4.6%
28005
 
4.9%
116538
10.1%
05298
 
3.2%

temp9am
Real number (ℝ)

HIGH CORRELATION

Distinct440
Distinct (%)0.3%
Missing976
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean16.94941986
Minimum-7.2
Maximum40.2
Zeros38
Zeros (%)< 0.1%
Memory size2.5 MiB
2021-05-09T17:45:02.521438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-7.2
5-th percentile6.9
Q112.2
median16.6
Q321.5
95-th percentile28.2
Maximum40.2
Range47.4
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation6.494966589
Coefficient of variation (CV)0.383196985
Kurtosis-0.3447351777
Mean16.94941986
Median Absolute Deviation (MAD)4.6
Skewness0.1036326171
Sum2769704.7
Variance42.184591
MonotocityNot monotonic
2021-05-09T17:45:02.675274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.81042
 
0.6%
14.81018
 
0.6%
161016
 
0.6%
171013
 
0.6%
14995
 
0.6%
15992
 
0.6%
16.6985
 
0.6%
15.4974
 
0.6%
16.5972
 
0.6%
15.1969
 
0.6%
Other values (430)153434
93.3%
(Missing)976
 
0.6%
ValueCountFrequency (%)
-7.22
 
< 0.1%
-72
 
< 0.1%
-6.21
 
< 0.1%
-5.91
 
< 0.1%
-5.63
< 0.1%
-5.52
 
< 0.1%
-5.32
 
< 0.1%
-5.26
< 0.1%
-4.81
 
< 0.1%
-4.53
< 0.1%
ValueCountFrequency (%)
40.21
< 0.1%
39.41
< 0.1%
39.11
< 0.1%
391
< 0.1%
38.91
< 0.1%
38.61
< 0.1%
38.31
< 0.1%
38.21
< 0.1%
381
< 0.1%
37.91
< 0.1%

temp3pm
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct500
Distinct (%)0.3%
Missing4077
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean21.63317219
Minimum-5.4
Maximum46.7
Zeros17
Zeros (%)< 0.1%
Memory size2.5 MiB
2021-05-09T17:45:02.836772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-5.4
5-th percentile11.5
Q116.6
median21.1
Q326.4
95-th percentile33.6
Maximum46.7
Range52.1
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation6.932849064
Coefficient of variation (CV)0.3204730681
Kurtosis-0.1349960626
Mean21.63317219
Median Absolute Deviation (MAD)4.8
Skewness0.2427139888
Sum3467992.2
Variance48.06439614
MonotocityNot monotonic
2021-05-09T17:45:02.998266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.4983
 
0.6%
20983
 
0.6%
18.5982
 
0.6%
19968
 
0.6%
17.8967
 
0.6%
19.3951
 
0.6%
18943
 
0.6%
18.6935
 
0.6%
17934
 
0.6%
19.4932
 
0.6%
Other values (490)150731
91.7%
(Missing)4077
 
2.5%
ValueCountFrequency (%)
-5.42
< 0.1%
-5.11
 
< 0.1%
-4.41
 
< 0.1%
-4.22
< 0.1%
-4.11
 
< 0.1%
-41
 
< 0.1%
-3.92
< 0.1%
-3.82
< 0.1%
-3.74
< 0.1%
-3.54
< 0.1%
ValueCountFrequency (%)
46.72
< 0.1%
46.22
< 0.1%
46.13
< 0.1%
45.91
 
< 0.1%
45.82
< 0.1%
45.41
 
< 0.1%
45.32
< 0.1%
45.23
< 0.1%
452
< 0.1%
44.91
 
< 0.1%

raintoday
Categorical

Distinct2
Distinct (%)< 0.1%
Missing1619
Missing (%)1.0%
Memory size10.6 MiB
0.0
126200 
1.0
36567 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters488301
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0126200
76.8%
1.036567
 
22.2%
(Missing)1619
 
1.0%
2021-05-09T17:45:03.326882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-09T17:45:03.445730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0126200
77.5%
1.036567
 
22.5%

Most occurring characters

ValueCountFrequency (%)
0288967
59.2%
.162767
33.3%
136567
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number325534
66.7%
Other Punctuation162767
33.3%

Most frequent character per category

ValueCountFrequency (%)
0288967
88.8%
136567
 
11.2%
ValueCountFrequency (%)
.162767
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common488301
100.0%

Most frequent character per script

ValueCountFrequency (%)
0288967
59.2%
.162767
33.3%
136567
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII488301
100.0%

Most frequent character per block

ValueCountFrequency (%)
0288967
59.2%
.162767
33.3%
136567
 
7.5%

amntraintmrw
Real number (ℝ≥0)

ZEROS

Distinct681
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.363259645
Minimum0
Maximum371
Zeros105038
Zeros (%)63.9%
Memory size2.5 MiB
2021-05-09T17:45:03.575153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.8
95-th percentile13
Maximum371
Range371
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation8.419366365
Coefficient of variation (CV)3.562607428
Kurtosis171.28584
Mean2.363259645
Median Absolute Deviation (MAD)0
Skewness9.66526212
Sum388486.8
Variance70.88572998
MonotocityNot monotonic
2021-05-09T17:45:03.737340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0105038
63.9%
0.210263
 
6.2%
0.44350
 
2.6%
0.62955
 
1.8%
0.82345
 
1.4%
12016
 
1.2%
1.21771
 
1.1%
1.41615
 
1.0%
1.61393
 
0.8%
1.81266
 
0.8%
Other values (671)31374
 
19.1%
ValueCountFrequency (%)
0105038
63.9%
0.1200
 
0.1%
0.210263
 
6.2%
0.385
 
0.1%
0.44350
 
2.6%
0.551
 
< 0.1%
0.62955
 
1.8%
0.716
 
< 0.1%
0.82345
 
1.4%
0.919
 
< 0.1%
ValueCountFrequency (%)
3711
< 0.1%
367.61
< 0.1%
278.41
< 0.1%
268.61
< 0.1%
247.21
< 0.1%
2401
< 0.1%
236.81
< 0.1%
2251
< 0.1%
219.62
< 0.1%
216.31
< 0.1%

raintomorrow
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
0
127338 
1
37048 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters164386
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0127338
77.5%
137048
 
22.5%
2021-05-09T17:45:04.099279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-09T17:45:04.216087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0127338
77.5%
137048
 
22.5%

Most occurring characters

ValueCountFrequency (%)
0127338
77.5%
137048
 
22.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number164386
100.0%

Most frequent character per category

ValueCountFrequency (%)
0127338
77.5%
137048
 
22.5%

Most occurring scripts

ValueCountFrequency (%)
Common164386
100.0%

Most frequent character per script

ValueCountFrequency (%)
0127338
77.5%
137048
 
22.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII164386
100.0%

Most frequent character per block

ValueCountFrequency (%)
0127338
77.5%
137048
 
22.5%

temp
Real number (ℝ)

Distinct7620
Distinct (%)4.6%
Missing397
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean28.47026563
Minimum-3.76
Maximum59.72
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-05-09T17:45:04.351358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-3.76
5-th percentile3.473824179
Q122.52
median28.52
Q335.36
95-th percentile44.36
Maximum59.72
Range63.48
Interquartile range (IQR)12.84

Descriptive statistics

Standard deviation10.22528483
Coefficient of variation (CV)0.359156636
Kurtosis0.4721549931
Mean28.47026563
Median Absolute Deviation (MAD)6.36
Skewness-0.4141530851
Sum4668810.391
Variance104.5564499
MonotocityNot monotonic
2021-05-09T17:45:04.503184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26972
 
0.6%
24.8916
 
0.6%
25.76914
 
0.6%
26.48884
 
0.5%
24.68883
 
0.5%
27.2879
 
0.5%
26.96878
 
0.5%
25.4873
 
0.5%
25.88872
 
0.5%
25.28871
 
0.5%
Other values (7610)155047
94.3%
ValueCountFrequency (%)
-3.762
< 0.1%
-2.921
 
< 0.1%
-2.561
 
< 0.1%
-1.841
 
< 0.1%
-1.722
< 0.1%
-1.62
< 0.1%
-1.481
 
< 0.1%
-1.241
 
< 0.1%
-13
< 0.1%
-0.881
 
< 0.1%
ValueCountFrequency (%)
59.721
 
< 0.1%
58.763
< 0.1%
58.42
 
< 0.1%
58.282
 
< 0.1%
58.164
< 0.1%
58.041
 
< 0.1%
57.922
 
< 0.1%
57.81
 
< 0.1%
57.685
< 0.1%
57.563
< 0.1%

humidity
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct4163
Distinct (%)2.6%
Missing5124
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean62.21218436
Minimum2
Maximum122
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-05-09T17:45:04.677300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile16.4
Q144
median63.2
Q380
95-th percentile106.4
Maximum122
Range120
Interquartile range (IQR)36

Descriptive statistics

Standard deviation26.62052964
Coefficient of variation (CV)0.4278989705
Kurtosis-0.3860322964
Mean62.21218436
Median Absolute Deviation (MAD)18
Skewness-0.1243788503
Sum9908036.906
Variance708.6525983
MonotocityNot monotonic
2021-05-09T17:45:04.857263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.43042
 
1.9%
64.43030
 
1.8%
683011
 
1.8%
65.62939
 
1.8%
72.82935
 
1.8%
71.62918
 
1.8%
69.22909
 
1.8%
742880
 
1.8%
622869
 
1.7%
66.82869
 
1.7%
Other values (4153)129860
79.0%
(Missing)5124
 
3.1%
ValueCountFrequency (%)
24
< 0.1%
2.000285661
 
< 0.1%
2.0005134141
 
< 0.1%
2.0010653111
 
< 0.1%
2.0011859811
 
< 0.1%
2.0014173711
 
< 0.1%
2.0015754412
< 0.1%
2.0015883981
 
< 0.1%
2.0016116281
 
< 0.1%
2.0017873771
 
< 0.1%
ValueCountFrequency (%)
122524
0.3%
120.8478
0.3%
119.6664
0.4%
118.4452
0.3%
117.2511
0.3%
116499
0.3%
114.8604
0.4%
113.6660
0.4%
112.4725
0.4%
111.2678
0.4%

precipitation3pm
Real number (ℝ≥0)

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.01461195
Minimum0
Maximum26
Zeros17
Zeros (%)< 0.1%
Memory size2.5 MiB
2021-05-09T17:45:05.031377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median10
Q312
95-th percentile15
Maximum26
Range26
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.174690898
Coefficient of variation (CV)0.3170058824
Kurtosis0.1043920325
Mean10.01461195
Median Absolute Deviation (MAD)2
Skewness0.3205194297
Sum1646262
Variance10.0786623
MonotocityNot monotonic
2021-05-09T17:45:05.189135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
920467
12.5%
1020309
12.4%
1118736
11.4%
818495
11.3%
1215593
9.5%
714850
9.0%
1312033
7.3%
610431
6.3%
148584
5.2%
56107
 
3.7%
Other values (17)18781
11.4%
ValueCountFrequency (%)
017
 
< 0.1%
169
 
< 0.1%
2370
 
0.2%
31271
 
0.8%
43073
 
1.9%
56107
 
3.7%
610431
6.3%
714850
9.0%
818495
11.3%
920467
12.5%
ValueCountFrequency (%)
264
 
< 0.1%
255
 
< 0.1%
248
 
< 0.1%
2337
 
< 0.1%
2273
 
< 0.1%
21155
 
0.1%
20330
 
0.2%
19612
 
0.4%
181188
0.7%
172130
1.3%

precipitation9am
Real number (ℝ)

Distinct142193
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.995437942
Minimum-17.73934574
Maximum32.47858981
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-05-09T17:45:05.415362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-17.73934574
5-th percentile1.763924092
Q16.64601366
median9.996363073
Q313.37960089
95-th percentile18.22206579
Maximum32.47858981
Range50.21793555
Interquartile range (IQR)6.733587232

Descriptive statistics

Standard deviation4.998239211
Coefficient of variation (CV)0.5000520477
Kurtosis0.003843435433
Mean9.995437942
Median Absolute Deviation (MAD)3.367733616
Skewness-0.006724031779
Sum1643110.062
Variance24.98239521
MonotocityNot monotonic
2021-05-09T17:45:05.592678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.7876600862
 
< 0.1%
8.5708873632
 
< 0.1%
6.3619113492
 
< 0.1%
13.956431512
 
< 0.1%
15.227773742
 
< 0.1%
13.990528372
 
< 0.1%
-0.12905090342
 
< 0.1%
9.7331702642
 
< 0.1%
13.444119062
 
< 0.1%
10.556307032
 
< 0.1%
Other values (142183)164366
> 99.9%
ValueCountFrequency (%)
-17.739345741
< 0.1%
-11.035403711
< 0.1%
-11.023931311
< 0.1%
-10.698081062
< 0.1%
-10.302856511
< 0.1%
-9.9785583611
< 0.1%
-9.8610731481
< 0.1%
-9.7010815511
< 0.1%
-9.506687341
< 0.1%
-9.3890278942
< 0.1%
ValueCountFrequency (%)
32.478589811
< 0.1%
32.21008071
< 0.1%
30.355796971
< 0.1%
30.243844882
< 0.1%
30.174273091
< 0.1%
28.893262451
< 0.1%
28.781074451
< 0.1%
28.656045181
< 0.1%
28.614592012
< 0.1%
28.172268741
< 0.1%

modelo_vigente
Real number (ℝ≥0)

Distinct130279
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2302066959
Minimum0
Maximum0.9994
Zeros471
Zeros (%)0.3%
Memory size2.5 MiB
2021-05-09T17:45:05.826719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004595059479
Q10.03225130191
median0.1083058078
Q30.3374927775
95-th percentile0.8478924242
Maximum0.9994
Range0.9994
Interquartile range (IQR)0.3052414756

Descriptive statistics

Standard deviation0.2691578395
Coefficient of variation (CV)1.169200741
Kurtosis0.5749034484
Mean0.2302066959
Median Absolute Deviation (MAD)0.09300930289
Skewness1.326379389
Sum37842.75792
Variance0.07244594256
MonotocityNot monotonic
2021-05-09T17:45:06.008536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0471
 
0.3%
0.00194
 
0.1%
0.000666666666773
 
< 0.1%
0.000469
 
< 0.1%
0.000567
 
< 0.1%
0.00263
 
< 0.1%
0.000260
 
< 0.1%
0.000222222222259
 
< 0.1%
0.000333333333357
 
< 0.1%
0.0002550
 
< 0.1%
Other values (130269)163323
99.4%
ValueCountFrequency (%)
0471
0.3%
6.600660066 × 1063
 
< 0.1%
7.989347537 × 10636
 
< 0.1%
1.075268817 × 1051
 
< 0.1%
1.578947368 × 1051
 
< 0.1%
2.435312024 × 10512
 
< 0.1%
3.150787697 × 1057
 
< 0.1%
3.234246778 × 1053
 
< 0.1%
5.748190294 × 1053
 
< 0.1%
0.00010526315791
 
< 0.1%
ValueCountFrequency (%)
0.99941
< 0.1%
0.99926984131
< 0.1%
0.99913095241
< 0.1%
0.99833333331
< 0.1%
0.99807301591
< 0.1%
0.99782
< 0.1%
0.99773888891
< 0.1%
0.99771666671
< 0.1%
0.99768333331
< 0.1%
0.99767857141
< 0.1%

wind_gustdir
Categorical

MISSING

Distinct16
Distinct (%)< 0.1%
Missing127967
Missing (%)77.8%
Memory size7.2 MiB
SE
2625 
SW
2542 
W
2529 
S
2522 
N
2470 
Other values (11)
23731 

Length

Max length3
Median length2
Mean length2.199099371
Min length1

Characters and Unicode

Total characters80089
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowWNW
3rd rowWSW
4th rowNE
5th rowW
ValueCountFrequency (%)
SE2625
 
1.6%
SW2542
 
1.5%
W2529
 
1.5%
S2522
 
1.5%
N2470
 
1.5%
SSE2446
 
1.5%
WSW2434
 
1.5%
E2424
 
1.5%
SSW2298
 
1.4%
WNW2196
 
1.3%
Other values (6)11933
 
7.3%
(Missing)127967
77.8%
2021-05-09T17:45:06.380394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
se2625
 
7.2%
sw2542
 
7.0%
w2529
 
6.9%
s2522
 
6.9%
n2470
 
6.8%
sse2446
 
6.7%
wsw2434
 
6.7%
e2424
 
6.7%
ssw2298
 
6.3%
wnw2196
 
6.0%
Other values (6)11933
32.8%

Most occurring characters

ValueCountFrequency (%)
S21700
27.1%
W20532
25.6%
E19803
24.7%
N18054
22.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter80089
100.0%

Most frequent character per category

ValueCountFrequency (%)
S21700
27.1%
W20532
25.6%
E19803
24.7%
N18054
22.5%

Most occurring scripts

ValueCountFrequency (%)
Latin80089
100.0%

Most frequent character per script

ValueCountFrequency (%)
S21700
27.1%
W20532
25.6%
E19803
24.7%
N18054
22.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII80089
100.0%

Most frequent character per block

ValueCountFrequency (%)
S21700
27.1%
W20532
25.6%
E19803
24.7%
N18054
22.5%

wind_gustspeed
Real number (ℝ≥0)

MISSING

Distinct63
Distinct (%)0.2%
Missing127960
Missing (%)77.8%
Infinite0
Infinite (%)0.0%
Mean40.38744304
Minimum7
Maximum135
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-05-09T17:45:06.551555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile22
Q131
median39
Q348
95-th percentile65
Maximum135
Range128
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.53278336
Coefficient of variation (CV)0.3350740316
Kurtosis1.625654633
Mean40.38744304
Median Absolute Deviation (MAD)8
Skewness0.9130138439
Sum1471153
Variance183.1362256
MonotocityNot monotonic
2021-05-09T17:45:06.711566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
352498
 
1.5%
392448
 
1.5%
312279
 
1.4%
332180
 
1.3%
372143
 
1.3%
412090
 
1.3%
301855
 
1.1%
431829
 
1.1%
281687
 
1.0%
441534
 
0.9%
Other values (53)15883
 
9.7%
(Missing)127960
77.8%
ValueCountFrequency (%)
74
 
< 0.1%
914
 
< 0.1%
1127
 
< 0.1%
13126
 
0.1%
15190
 
0.1%
17367
 
0.2%
19405
 
0.2%
20661
0.4%
22744
0.5%
241045
0.6%
ValueCountFrequency (%)
1352
 
< 0.1%
1262
 
< 0.1%
1241
 
< 0.1%
1201
 
< 0.1%
1171
 
< 0.1%
1152
 
< 0.1%
1131
 
< 0.1%
1094
< 0.1%
1077
< 0.1%
1063
< 0.1%

wind_dir9am
Categorical

MISSING

Distinct16
Distinct (%)< 0.1%
Missing127454
Missing (%)77.5%
Memory size7.2 MiB
N
3113 
SE
2663 
E
2633 
SSE
2538 
W
2367 
Other values (11)
23618 

Length

Max length3
Median length2
Mean length2.179762807
Min length1

Characters and Unicode

Total characters80503
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowNNW
3rd rowW
4th rowSE
5th rowENE
ValueCountFrequency (%)
N3113
 
1.9%
SE2663
 
1.6%
E2633
 
1.6%
SSE2538
 
1.5%
W2367
 
1.4%
S2350
 
1.4%
NW2282
 
1.4%
ENE2280
 
1.4%
SW2265
 
1.4%
NNE2197
 
1.3%
Other values (6)12244
 
7.4%
(Missing)127454
77.5%
2021-05-09T17:45:07.121363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n3113
 
8.4%
se2663
 
7.2%
e2633
 
7.1%
sse2538
 
6.9%
w2367
 
6.4%
s2350
 
6.4%
nw2282
 
6.2%
ene2280
 
6.2%
sw2265
 
6.1%
nne2197
 
5.9%
Other values (6)12244
33.2%

Most occurring characters

ValueCountFrequency (%)
E20862
25.9%
N20530
25.5%
S20311
25.2%
W18800
23.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter80503
100.0%

Most frequent character per category

ValueCountFrequency (%)
E20862
25.9%
N20530
25.5%
S20311
25.2%
W18800
23.4%

Most occurring scripts

ValueCountFrequency (%)
Latin80503
100.0%

Most frequent character per script

ValueCountFrequency (%)
E20862
25.9%
N20530
25.5%
S20311
25.2%
W18800
23.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII80503
100.0%

Most frequent character per block

ValueCountFrequency (%)
E20862
25.9%
N20530
25.5%
S20311
25.2%
W18800
23.4%

wind_dir3pm
Categorical

MISSING

Distinct16
Distinct (%)< 0.1%
Missing125396
Missing (%)76.3%
Memory size7.3 MiB
SE
3244 
S
2706 
W
2656 
SW
2643 
WSW
2637 
Other values (11)
25104 

Length

Max length3
Median length2
Mean length2.204821749
Min length1

Characters and Unicode

Total characters85966
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWNW
2nd rowWSW
3rd rowWSW
4th rowE
5th rowNW
ValueCountFrequency (%)
SE3244
 
2.0%
S2706
 
1.6%
W2656
 
1.6%
SW2643
 
1.6%
WSW2637
 
1.6%
SSE2611
 
1.6%
E2454
 
1.5%
WNW2417
 
1.5%
N2404
 
1.5%
ESE2354
 
1.4%
Other values (6)12864
 
7.8%
(Missing)125396
76.3%
2021-05-09T17:45:07.575272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
se3244
 
8.3%
s2706
 
6.9%
w2656
 
6.8%
sw2643
 
6.8%
wsw2637
 
6.8%
sse2611
 
6.7%
e2454
 
6.3%
wnw2417
 
6.2%
n2404
 
6.2%
ese2354
 
6.0%
Other values (6)12864
33.0%

Most occurring characters

ValueCountFrequency (%)
S23208
27.0%
W21942
25.5%
E21553
25.1%
N19263
22.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter85966
100.0%

Most frequent character per category

ValueCountFrequency (%)
S23208
27.0%
W21942
25.5%
E21553
25.1%
N19263
22.4%

Most occurring scripts

ValueCountFrequency (%)
Latin85966
100.0%

Most frequent character per script

ValueCountFrequency (%)
S23208
27.0%
W21942
25.5%
E21553
25.1%
N19263
22.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII85966
100.0%

Most frequent character per block

ValueCountFrequency (%)
S23208
27.0%
W21942
25.5%
E21553
25.1%
N19263
22.4%

wind_speed9am
Real number (ℝ≥0)

MISSING
ZEROS

Distinct39
Distinct (%)0.1%
Missing125086
Missing (%)76.1%
Infinite0
Infinite (%)0.0%
Mean14.14564885
Minimum0
Maximum87
Zeros2351
Zeros (%)1.4%
Memory size2.5 MiB
2021-05-09T17:45:07.757493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median13
Q319
95-th percentile30
Maximum87
Range87
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.928349253
Coefficient of variation (CV)0.6311728324
Kurtosis1.188131273
Mean14.14564885
Median Absolute Deviation (MAD)6
Skewness0.7820819194
Sum555924
Variance79.71542039
MonotocityNot monotonic
2021-05-09T17:45:07.905417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
93693
 
2.2%
133558
 
2.2%
113245
 
2.0%
73007
 
1.8%
152991
 
1.8%
172885
 
1.8%
62530
 
1.5%
192463
 
1.5%
02351
 
1.4%
202203
 
1.3%
Other values (29)10374
 
6.3%
(Missing)125086
76.1%
ValueCountFrequency (%)
02351
1.4%
21213
 
0.7%
41581
1.0%
62530
1.5%
73007
1.8%
93693
2.2%
113245
2.0%
133558
2.2%
152991
1.8%
172885
1.8%
ValueCountFrequency (%)
871
 
< 0.1%
831
 
< 0.1%
743
 
< 0.1%
671
 
< 0.1%
652
 
< 0.1%
633
 
< 0.1%
615
< 0.1%
576
< 0.1%
569
< 0.1%
5411
< 0.1%

wind_speed3pm
Real number (ℝ≥0)

MISSING

Distinct41
Distinct (%)0.1%
Missing125058
Missing (%)76.1%
Infinite0
Infinite (%)0.0%
Mean18.9489168
Minimum0
Maximum87
Zeros318
Zeros (%)0.2%
Memory size2.5 MiB
2021-05-09T17:45:08.084220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q113
median19
Q324
95-th percentile35
Maximum87
Range87
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.774876547
Coefficient of variation (CV)0.4630806414
Kurtosis0.7977314
Mean18.9489168
Median Absolute Deviation (MAD)6
Skewness0.5884125002
Sum745223
Variance76.99845841
MonotocityNot monotonic
2021-05-09T17:45:08.229046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
173437
 
2.1%
133340
 
2.0%
203291
 
2.0%
153185
 
1.9%
193174
 
1.9%
112611
 
1.6%
242563
 
1.6%
222531
 
1.5%
92504
 
1.5%
261896
 
1.2%
Other values (31)10796
 
6.6%
(Missing)125058
76.1%
ValueCountFrequency (%)
0318
 
0.2%
2271
 
0.2%
4558
 
0.3%
6975
 
0.6%
71561
0.9%
92504
1.5%
112611
1.6%
133340
2.0%
153185
1.9%
173437
2.1%
ValueCountFrequency (%)
871
 
< 0.1%
831
 
< 0.1%
762
 
< 0.1%
691
 
< 0.1%
671
 
< 0.1%
654
< 0.1%
631
 
< 0.1%
616
< 0.1%
597
< 0.1%
576
< 0.1%

windgustdir
Categorical

MISSING

Distinct16
Distinct (%)< 0.1%
Missing46997
Missing (%)28.6%
Memory size9.3 MiB
W
8922 
N
8043 
SE
8029 
E
8022 
WSW
7886 
Other values (11)
76487 

Length

Max length3
Median length2
Mean length2.196023477
Min length1

Characters and Unicode

Total characters257789
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowW
3rd rowE
4th rowE
5th rowS
ValueCountFrequency (%)
W8922
 
5.4%
N8043
 
4.9%
SE8029
 
4.9%
E8022
 
4.9%
WSW7886
 
4.8%
SSE7793
 
4.7%
SSW7780
 
4.7%
S7647
 
4.7%
SW7616
 
4.6%
NW7294
 
4.4%
Other values (6)38357
23.3%
(Missing)46997
28.6%
2021-05-09T17:45:08.590518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
w8922
 
7.6%
n8043
 
6.9%
se8029
 
6.8%
e8022
 
6.8%
wsw7886
 
6.7%
sse7793
 
6.6%
ssw7780
 
6.6%
s7647
 
6.5%
sw7616
 
6.5%
nw7294
 
6.2%
Other values (6)38357
32.7%

Most occurring characters

ValueCountFrequency (%)
S68624
26.6%
W67780
26.3%
E62285
24.2%
N59100
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter257789
100.0%

Most frequent character per category

ValueCountFrequency (%)
S68624
26.6%
W67780
26.3%
E62285
24.2%
N59100
22.9%

Most occurring scripts

ValueCountFrequency (%)
Latin257789
100.0%

Most frequent character per script

ValueCountFrequency (%)
S68624
26.6%
W67780
26.3%
E62285
24.2%
N59100
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII257789
100.0%

Most frequent character per block

ValueCountFrequency (%)
S68624
26.6%
W67780
26.3%
E62285
24.2%
N59100
22.9%

windgustspeed
Real number (ℝ≥0)

MISSING

Distinct66
Distinct (%)0.1%
Missing46944
Missing (%)28.6%
Infinite0
Infinite (%)0.0%
Mean39.70169105
Minimum6
Maximum135
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-05-09T17:45:08.767135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile20
Q130
median37
Q348
95-th percentile65
Maximum135
Range129
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.64432793
Coefficient of variation (CV)0.3436712031
Kurtosis1.316042277
Mean39.70169105
Median Absolute Deviation (MAD)7
Skewness0.8614914618
Sum4662646
Variance186.1676846
MonotocityNot monotonic
2021-05-09T17:45:08.964166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
357967
 
4.8%
397448
 
4.5%
317385
 
4.5%
377021
 
4.3%
336854
 
4.2%
416218
 
3.8%
306205
 
3.8%
285756
 
3.5%
435624
 
3.4%
464628
 
2.8%
Other values (56)52336
31.8%
(Missing)46944
28.6%
ValueCountFrequency (%)
61
 
< 0.1%
715
 
< 0.1%
995
 
0.1%
11197
 
0.1%
13512
 
0.3%
15811
 
0.5%
171292
0.8%
191668
1.0%
202454
1.5%
222543
1.5%
ValueCountFrequency (%)
1351
 
< 0.1%
1301
 
< 0.1%
1241
 
< 0.1%
1222
 
< 0.1%
1202
 
< 0.1%
1173
 
< 0.1%
1153
 
< 0.1%
1139
< 0.1%
1114
< 0.1%
1095
< 0.1%

winddir9am
Categorical

MISSING

Distinct16
Distinct (%)< 0.1%
Missing48360
Missing (%)29.4%
Memory size9.3 MiB
N
10181 
SE
7840 
E
7764 
NW
7757 
SSE
7751 
Other values (11)
74733 

Length

Max length3
Median length2
Mean length2.187121852
Min length1

Characters and Unicode

Total characters253763
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSE
2nd rowE
3rd rowNNE
4th rowWSW
5th rowS
ValueCountFrequency (%)
N10181
 
6.2%
SE7840
 
4.8%
E7764
 
4.7%
NW7757
 
4.7%
SSE7751
 
4.7%
S7313
 
4.4%
SW7297
 
4.4%
W7206
 
4.4%
NNE7081
 
4.3%
NNW7016
 
4.3%
Other values (6)38820
23.6%
(Missing)48360
29.4%
2021-05-09T17:45:09.402005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n10181
 
8.8%
se7840
 
6.8%
e7764
 
6.7%
nw7757
 
6.7%
sse7751
 
6.7%
s7313
 
6.3%
sw7297
 
6.3%
w7206
 
6.2%
nne7081
 
6.1%
nnw7016
 
6.0%
Other values (6)38820
33.5%

Most occurring characters

ValueCountFrequency (%)
N65604
25.9%
S63880
25.2%
E63339
25.0%
W60940
24.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter253763
100.0%

Most frequent character per category

ValueCountFrequency (%)
N65604
25.9%
S63880
25.2%
E63339
25.0%
W60940
24.0%

Most occurring scripts

ValueCountFrequency (%)
Latin253763
100.0%

Most frequent character per script

ValueCountFrequency (%)
N65604
25.9%
S63880
25.2%
E63339
25.0%
W60940
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII253763
100.0%

Most frequent character per block

ValueCountFrequency (%)
N65604
25.9%
S63880
25.2%
E63339
25.0%
W60940
24.0%

winddir3pm
Categorical

MISSING

Distinct16
Distinct (%)< 0.1%
Missing43825
Missing (%)26.7%
Memory size9.4 MiB
W
8867 
SE
8775 
S
8265 
WSW
8092 
SW
7987 
Other values (11)
78575 

Length

Max length3
Median length2
Mean length2.211386767
Min length1

Characters and Unicode

Total characters266607
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWSW
2nd rowW
3rd rowESE
4th rowENE
5th rowSSE
ValueCountFrequency (%)
W8867
 
5.4%
SE8775
 
5.3%
S8265
 
5.0%
WSW8092
 
4.9%
SW7987
 
4.9%
SSE7828
 
4.8%
N7700
 
4.7%
WNW7673
 
4.7%
NW7516
 
4.6%
ESE7269
 
4.4%
Other values (6)40589
24.7%
(Missing)43825
26.7%
2021-05-09T17:45:09.808471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
w8867
 
7.4%
se8775
 
7.3%
s8265
 
6.9%
wsw8092
 
6.7%
sw7987
 
6.6%
sse7828
 
6.5%
n7700
 
6.4%
wnw7673
 
6.4%
nw7516
 
6.2%
ese7269
 
6.0%
Other values (6)40589
33.7%

Most occurring characters

ValueCountFrequency (%)
S70244
26.3%
W70099
26.3%
E64205
24.1%
N62059
23.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter266607
100.0%

Most frequent character per category

ValueCountFrequency (%)
S70244
26.3%
W70099
26.3%
E64205
24.1%
N62059
23.3%

Most occurring scripts

ValueCountFrequency (%)
Latin266607
100.0%

Most frequent character per script

ValueCountFrequency (%)
S70244
26.3%
W70099
26.3%
E64205
24.1%
N62059
23.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII266607
100.0%

Most frequent character per block

ValueCountFrequency (%)
S70244
26.3%
W70099
26.3%
E64205
24.1%
N62059
23.3%

windspeed9am
Real number (ℝ≥0)

MISSING
ZEROS

Distinct42
Distinct (%)< 0.1%
Missing40753
Missing (%)24.8%
Infinite0
Infinite (%)0.0%
Mean13.90123996
Minimum0
Maximum130
Zeros7570
Zeros (%)4.6%
Memory size2.5 MiB
2021-05-09T17:45:09.986144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median13
Q319
95-th percentile30
Maximum130
Range130
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.848133877
Coefficient of variation (CV)0.6364996146
Kurtosis1.45263166
Mean13.90123996
Median Absolute Deviation (MAD)6
Skewness0.7919918891
Sum1718652
Variance78.28947311
MonotocityNot monotonic
2021-05-09T17:45:10.161017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
912007
 
7.3%
1311361
 
6.9%
1110110
 
6.2%
179391
 
5.7%
79262
 
5.6%
159029
 
5.5%
67937
 
4.8%
07570
 
4.6%
197420
 
4.5%
206920
 
4.2%
Other values (32)32626
19.8%
(Missing)40753
24.8%
ValueCountFrequency (%)
07570
4.6%
24108
 
2.5%
45665
3.4%
67937
4.8%
79262
5.6%
912007
7.3%
1110110
6.2%
1311361
6.9%
159029
5.5%
179391
5.7%
ValueCountFrequency (%)
1302
 
< 0.1%
871
 
< 0.1%
742
 
< 0.1%
721
 
< 0.1%
692
 
< 0.1%
673
 
< 0.1%
658
< 0.1%
636
< 0.1%
619
< 0.1%
595
< 0.1%

windspeed3pm
Real number (ℝ≥0)

MISSING

Distinct41
Distinct (%)< 0.1%
Missing42911
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean18.45170611
Minimum0
Maximum83
Zeros882
Zeros (%)0.5%
Memory size2.5 MiB
2021-05-09T17:45:10.351001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q113
median17
Q324
95-th percentile33
Maximum83
Range83
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.778299542
Coefficient of variation (CV)0.4757445999
Kurtosis0.7780722111
Mean18.45170611
Median Absolute Deviation (MAD)6
Skewness0.6609954925
Sum2241421
Variance77.05854285
MonotocityNot monotonic
2021-05-09T17:45:10.499446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1311039
 
6.7%
1710753
 
6.5%
2010009
 
6.1%
159908
 
6.0%
199494
 
5.8%
118895
 
5.4%
98685
 
5.3%
247541
 
4.6%
227079
 
4.3%
285391
 
3.3%
Other values (31)32681
19.9%
(Missing)42911
26.1%
ValueCountFrequency (%)
0882
 
0.5%
2894
 
0.5%
41962
 
1.2%
63406
 
2.1%
75213
3.2%
98685
5.3%
118895
5.4%
1311039
6.7%
159908
6.0%
1710753
6.5%
ValueCountFrequency (%)
831
 
< 0.1%
781
 
< 0.1%
741
 
< 0.1%
722
 
< 0.1%
692
 
< 0.1%
6514
< 0.1%
6314
< 0.1%
6118
< 0.1%
5913
< 0.1%
5726
< 0.1%

Interactions

2021-05-09T17:42:43.409224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:43.628822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:43.829004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:44.031938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:44.261467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:44.469097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:44.669624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:44.887214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:45.086163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:45.288656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:45.517338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:45.726279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:45.924643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:46.159593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:46.374583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:46.588037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:46.799038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:47.007044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:47.178053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:47.359643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:47.599119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:47.782115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:48.023304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:48.255301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:48.484605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:48.683617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:48.889089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:49.088200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:49.322310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:49.536230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:49.748365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:49.960433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:50.153264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:50.325698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:50.522328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:50.724170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:50.926290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:51.155298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:51.374129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:51.594310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:51.808487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:52.025417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:52.201576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:52.386374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:52.555194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:52.740806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:52.944207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:53.140308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:53.337226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:53.558339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:53.761169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:53.954360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:54.192358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:54.416285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:54.736194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:54.973194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:55.193041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:55.390538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:55.610401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:55.829368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:56.046244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:56.293095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:56.522660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:56.759370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:56.989252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:57.219917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:57.417098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:57.623264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:57.810219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:58.019502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:58.233623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:58.438437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:58.618903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:58.813669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:59.008429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:59.206773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:59.436929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:59.642190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:42:59.849457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:00.065102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:00.279947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:00.471246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:00.667898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:00.893211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:01.082830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:01.302738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:01.515817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:01.729131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:01.936074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:02.144176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:02.344138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:02.552351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:02.763508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:02.986340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:03.275787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:03.465689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:03.675740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:03.870441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:04.062378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:04.277392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:04.499174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:04.698059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:04.902953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:05.112831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:05.324185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:05.518282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:05.711782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:43:05.918299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-05-09T17:44:30.558234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:30.768274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:30.940792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:31.112603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:31.309554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:31.494211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:31.659762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:31.856185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:32.021207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:32.184532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:32.373673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:32.546776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:32.727116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:32.901278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:33.076027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:33.249256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:33.431423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:33.588219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:33.731113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:33.879141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:34.051325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:34.292009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:34.489200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:34.688730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:34.867149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:35.078771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:35.294873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:35.508141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:35.743153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:35.973528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:36.162446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:36.360323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:36.569852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:36.774160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:36.987244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:37.209625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:37.439070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:37.653402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:37.864323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:38.051951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:38.199120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:38.346780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:38.514963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:38.723287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:38.957240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:39.186491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:39.381471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:39.580151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:39.772742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:40.009035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:40.218416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:40.407589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:40.601651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:40.792150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:40.979137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:41.193206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:41.391874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:41.572872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:41.781353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:41.997077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:42.199169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:42.408393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:42.606202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:42.787199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:42.931078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:43.072101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:43.245005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:43.451221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:43.634121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:43.849672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:44.028396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:44.203841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:44.413046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:44.637889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:44.826391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:45.017696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:45.239115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:45.454222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:45.623609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:45.805604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:45.991968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:46.172045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:46.361320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:46.551191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:46.752083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:46.944629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:47.126300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:47.291071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:47.437248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:47.580117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:47.749535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T17:44:47.983949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-05-09T17:45:10.689237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-09T17:45:11.349161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-09T17:45:12.005060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-09T17:45:12.689662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-05-09T17:44:48.635282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-09T17:44:52.377244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-05-09T17:44:54.376399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-05-09T17:44:56.910214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

datelocationmintempmaxtemprainfallevaporationsunshinehumidity9amhumidity3pmpressure9ampressure3pmcloud9amcloud3pmtemp9amtemp3pmraintodayamntraintmrwraintomorrowtemphumidityprecipitation3pmprecipitation9ammodelo_vigentewind_gustdirwind_gustspeedwind_dir9amwind_dir3pmwind_speed9amwind_speed3pmwindgustdirwindgustspeedwinddir9amwinddir3pmwindspeed9amwindspeed3pm
02008-12-01Albury13.422.90.6NaNNaN71.022.01007.71007.18.0NaN16.921.80.00.0029.4828.400000125.1153600.089825W44.0WWNW20.024.0NaNNaNNaNNaNNaNNaN
12008-12-02Albury7.425.10.0NaNNaN44.025.01010.61007.8NaNNaN17.224.30.00.0032.122.2085691021.4971000.023477WNW44.0NNWWSW4.022.0NaNNaNNaNNaNNaNNaN
22008-12-03Albury12.925.70.0NaNNaN38.030.01007.61008.7NaN2.021.023.20.00.0032.8438.0000001720.7828590.027580WSW46.0WWSW19.026.0NaNNaNNaNNaNNaNNaN
32008-12-04Albury9.228.00.0NaNNaN45.016.01017.61012.8NaNNaN18.126.50.01.0035.6021.200000812.0286460.023962NE24.0SEE11.09.0NaNNaNNaNNaNNaNNaN
42008-12-05Albury17.532.31.0NaNNaN82.033.01010.81006.07.08.017.829.70.00.2040.7641.600000911.8835460.220164W41.0ENENW7.020.0NaNNaNNaNNaNNaNNaN
52008-12-06Albury14.629.70.2NaNNaN55.023.01009.21005.4NaNNaN20.628.90.00.0037.6429.600000817.3209940.056883WNW56.0WW19.024.0NaNNaNNaNNaNNaNNaN
62008-12-07Albury14.325.00.0NaNNaN49.019.01009.61008.21.0NaN18.124.60.00.0032.0024.800000819.7912710.030004W50.0SWW20.024.0NaNNaNNaNNaNNaNNaN
72008-12-08Albury7.726.70.0NaNNaN48.019.01013.41010.1NaNNaN16.325.50.00.0034.0424.80000077.2070850.023000W35.0SSEW6.017.0NaNNaNNaNNaNNaNNaN
82008-12-09Albury9.731.90.0NaNNaN42.09.01008.91003.6NaNNaN18.330.20.01.4140.2812.800000191.5407020.414649NNW80.0SENW7.028.0NaNNaNNaNNaNNaNNaN
92008-12-10Albury13.130.11.4NaNNaN58.027.01007.01005.7NaNNaN20.128.21.00.0038.1234.40000078.2598330.064945W28.0SSSE15.011.0NaNNaNNaNNaNNaNNaN

Last rows

datelocationmintempmaxtemprainfallevaporationsunshinehumidity9amhumidity3pmpressure9ampressure3pmcloud9amcloud3pmtemp9amtemp3pmraintodayamntraintmrwraintomorrowtemphumidityprecipitation3pmprecipitation9ammodelo_vigentewind_gustdirwind_gustspeedwind_dir9amwind_dir3pmwind_speed9amwind_speed3pmwindgustdirwindgustspeedwinddir9amwinddir3pmwindspeed9amwindspeed3pm
1643762017-06-20Uluru3.521.80.0NaNNaN59.027.01024.71021.2NaNNaN9.420.90.00.0028.1634.4125.8486810.002556NaNNaNNaNNaNNaNNaNE31.0ESEE15.013.0
1643772017-06-20Uluru3.521.80.0NaNNaN59.027.01024.71021.2NaNNaN9.420.90.00.0028.1634.4125.8486810.002556NaNNaNNaNNaNNaNNaNE31.0ESEE15.013.0
1643782017-06-21Uluru2.823.40.0NaNNaN51.024.01024.61020.3NaNNaN10.122.40.00.0030.0830.8106.6538790.002053NaNNaNNaNNaNNaNNaNE31.0SEENE13.011.0
1643792017-06-21Uluru2.823.40.0NaNNaN51.024.01024.61020.3NaNNaN10.122.40.00.0030.0830.8106.6538790.002053NaNNaNNaNNaNNaNNaNE31.0SEENE13.011.0
1643802017-06-22Uluru3.625.30.0NaNNaN56.021.01023.51019.1NaNNaN10.924.50.00.0032.3627.2919.7159760.023350NaNNaNNaNNaNNaNNaNNNW22.0SEN13.09.0
1643812017-06-22Uluru3.625.30.0NaNNaN56.021.01023.51019.1NaNNaN10.924.50.00.0032.3627.2919.7159760.023350NaNNaNNaNNaNNaNNaNNNW22.0SEN13.09.0
1643822017-06-23Uluru5.426.90.0NaNNaN53.024.01021.01016.8NaNNaN12.526.10.00.0034.2830.8120.9855510.007195NaNNaNNaNNaNNaNNaNN37.0SEWNW9.09.0
1643832017-06-23Uluru5.426.90.0NaNNaN53.024.01021.01016.8NaNNaN12.526.10.00.0034.2830.8120.9855510.007195NaNNaNNaNNaNNaNNaNN37.0SEWNW9.09.0
1643842017-06-24Uluru7.827.00.0NaNNaN51.024.01019.41016.53.02.015.126.00.00.0034.4030.8154.3814810.018811NaNNaNNaNNaNNaNNaNSE28.0SSEN13.07.0
1643852017-06-24Uluru7.827.00.0NaNNaN51.024.01019.41016.53.02.015.126.00.00.0034.4030.8154.3814810.018811NaNNaNNaNNaNNaNNaNSE28.0SSEN13.07.0